The field of anomaly detection and deepfake defense is rapidly evolving, with a focus on developing innovative methods to detect and prevent manipulated multimedia content. Recent research has explored the use of wavelet transforms, multimodal models, and spatial-frequency aware fusion networks to improve detection accuracy and efficiency. These advancements have significant implications for industrial inspection, social media, and national security. Noteworthy papers include: Wavelet-Enhanced PaDiM for Industrial Anomaly Detection, which integrates wavelet analysis with convolutional neural networks to improve anomaly detection and localization. ERF-BA-TFD+, a multimodal model that combines audio and video features to detect deepfakes, achieving state-of-the-art results on the DDL-AV dataset. ClearMask, a noise-free defense mechanism that modifies audio mel-spectrograms to prevent voice deepfake attacks, demonstrating effectiveness against unseen voice synthesis models and black-box API services.